SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 651–675 of 800 papers

TitleStatusHype
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network—0
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analyticsCode0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
Detecting malicious logins as graph anomalies—0
Walling up Backdoors in Intrusion Detection SystemsCode0
Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks—0
A Transfer Learning Approach for Network Intrusion Detection—0
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction—0
SynGAN: Towards Generating Synthetic Network Attacks using GANs—0
Sparse Bayesian approach for metric learning in latent spaceCode0
Omni SCADA Intrusion Detection Using Deep Learning Algorithms—0
Road Context-aware Intrusion Detection System for Autonomous Cars—0
Learning Neural Representations for Network Anomaly DetectionCode0
Data Analysis of Wireless Networks Using Classification Techniques—0
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
Using Temporal and Topological Features for Intrusion Detection in Operational Networks—0
Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle—0
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
Deep Reinforcement Learning for Cyber Security—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data—0
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things—0
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified